We present a robust approach for computing disparity maps with a supervised learning-based confidence prediction. This approach takes into consideration following features. First, we analyze the characteristics of various confidence measures in the random forest framework to select effective confidence measures depending on the characteristics of the training data and matching strategies, such as similarity measures and parameters. We then train a random forest using the selected confidence measures to improve the efficiency of confidence prediction and to build a better prediction model. Second, we present a confidence-based matching cost modulation scheme, based on predicted confidence values, to improve the robustness and accuracy of the (semi-) global stereo matching algorithms. Finally, we apply the proposed modulation scheme to popularly used algorithms to make them robust against unexpected difficulties that could occur in an uncontrolled environment using challenging outdoor datasets. The proposed confidence measure selection and cost modulation schemes are experimentally verified from various perspectives using the KITTI and Middlebury datasets.

中文翻译：

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学习和选择可靠的立体声匹配置信度

我们提出了一种基于监督的基于学习的置信度预测来计算视差图的可靠方法。此方法考虑了以下功能。首先，我们分析随机森林框架中各种置信度的特征，以根据训练数据和匹配策略（例如相似度和参数）的特征选择有效的置信度。然后，我们使用选定的置信度来训练随机森林，以提高置信度预测的效率并建立更好的预测模型。其次，我们基于预测的置信度值提出一种基于置信度的匹配成本调制方案，以提高（半）全局立体声匹配算法的鲁棒性和准确性。最后，我们将拟议的调制方案应用于流行的算法，以使它们在使用具有挑战性的户外数据集而不受控制的环境中，能够应对意外的困难。使用KITTI和Middlebury数据集从各种角度对所提出的置信度选择和成本调制方案进行了实验验证。